The present invention generally relates to ways of characterizing health related disorders. More particularly, the invention provides a system and method for visualizing information related to the sleep of an organism such as a mammal or human being. But it would be recognized that the invention has a much broader range of applicability such as applicability in situations where body position is a consideration.
Several disorders of sleep are known, including but not limited to snoring, insomnia, restless legs syndrome, upper airway resistance syndrome (UARS), and sleep apnea and its subtypes: obstructive sleep apnea (OSA) and central sleep apnea (CSA). To characterize disorders afflicting patients during sleep, diagnostic tests known as “sleep studies” may be performed. During a typical sleep study, physiological data are collected from the patient by various physiological sensors during a night's sleep. A type of sleep study called polysomnography (PSG) normally collects physiological data from a plurality of data channels over several hours. Belcher (Sleeping: On the Job! 2002, page 138) describes 16 to 18 different data channels for a typical PSG study. The resulting data set may be large. Lipman (Snoring from A to Zzzz. 1998, page 115) reports that a paper record of a PSG study may require one-half mile of paper. Computers and digital data storage have, in many cases, reduced the need for paper in sleep studies, but the quantity of information resulting from a sleep study may still tax the patience of a busy health care professional who wants to rapidly assess the clinical implications of the data.
Efficiently presenting a large data set to a busy health professional can be challenging. Much of the data collected during a sleep study are quantitative. Presenting quantitative data graphically has often proven advantageous. Tufte (The Display of Quantitative Information. 1983, page 9) notes: “Modern data graphics can do much more than simply substitute for small statistical tables. At their best, graphics are instruments for reasoning about quantitative information. Often the most effective way to describe, explore, and summarize a set of numbers —even a very large set—is to look at pictures of those numbers. Furthermore, of all methods for analyzing and communicating statistical information, well-designed data graphics are usually the simplest and at the same time the most powerful.”
Well-designed data graphics are, of course, generally advantageous, and Tufte has spent considerable effort teaching the principles of good data graphical design. He believes (Tufte. Supra. Page 13) graphical displays should, among other desiderata:                show the data;        avoid distorting what the data have to say;        present many numbers in a small place;        make large data sets coherent;        encourage the eye to compare different pieces of data;        reveal the data at several levels of detail, from a broad overview to the fine.        
Data from sleep studies have been displayed in a plurality of graphical formats, often satisfying Tufte's desiderata only partially.
FIG. 1A shows a segment of raw PSG data rendered graphically (from Undevia et al. Internet document, 2004). At least 17 channels of physiological data are presented, graphed in separate panes on a common horizontal (time) axis, with each pane having its own vertical axis. The top 8 panes represent electroencephalographic channels, with successive panes representing the left oculogram (the “LOC” pane of the graph, as labeled at the left margin), the right oculogram (ROC), chin electromyography (EMG chin), left and right leg electromyography (LAT/RAT), nasal airflow (Airflow), thoracic respiratory movement (Chest), abdominal respiratory movement (Abdomen), electrocardiogram (ECG), and arterial oxygen saturation (SAO2). Tufte advocates graphical designs that “encourage the eye to compare different pieces of data,” but the relatively large vertical distance between some channel plots in this figure generally makes inter-channel comparisons less inviting. Approximately 5 minutes of data are presented in FIG. 1A. Because a PSG study may collect data for 8 hours or longer, on the order of 100 such pages may be required to fully present a single study.
FIG. 1B shows approximately 6 hours 43 minutes of four data channels from a PSG study, plotted in four separate panes (from Undevia et al. Supra). From top to bottom the panes plot sleep stage, oxygen saturation, apnea-hypopnea event types, and delivered facemask pressure against time. Some of these data inherently vary slowly, allowing longer periods of time to be plotted in a given space without losing resolution. Plotting certain types of data, e.g. electrocardiogram signals, at the time scale of FIG. 1B would typically be far less informative because such data signals inherently vary faster. Although FIG. 1B needs only one page to plot results from the entire time of a sleep study, it appears to have a lower information density than FIG. 1A. Thus, FIG. 1B may have a potential for application of Tufte's desideratum to “present many numbers in a small place.” Another shortcoming of FIG. 1B is, as in FIG. 1A, the relatively large vertical distance between channel panes, making inter-channel comparisons generally less inviting.
In addition to polysomnography, other types of sleep studies may be performed. For example, several types of “reduced sensor” diagnostic devices collect fewer channels of physiological data than typical polysomnography. A certain tension often exists in designing a reduced sensor device between maximizing diagnostic yield and minimizing technical failures. In many cases diagnostic yield increases with the number of sensors used to collect physiological data from a patient being tested with the device. However, in many cases the likelihood of a technical failure during a study also increases with the number of sensors used. Thus, the choice of which sensors to design into a reduced sensor device is often critical. As Douglas (Sleep Med Rev. 2003;7:53-59) remarks: “The choice of sensors to be used is open to considerable debate.”
The American Academy of Sleep Medicine provides some guidance about sensor selection. A committee writing on their behalf states “Body position must be documented during recordings to assess the presence of OSA” (Thorpy et al. Sleep. 1994;17:372-377). There is evidence that the severity of OSA in some people varies according to their body position during sleep. In such persons, OSA is typically more severe when the person is lying on their back, as opposed to lying on a side or face down.
If positional data are collected during a sleep study, it is often desirable to visualize these data. FIG. 2 shows a plot 200 of a patient's body position during a night of sleep, as recorded by a reduced sensor device. Time, in minutes, is on the horizontal axis 210, and body position is on the vertical axis 220. Four body positions are recognized by this reduced sensor device, corresponding to being face up, face down, facing left, or facing right, as shown in labeling of vertical axis 220. The plot shows, for example, that initially the patient was facing right for a little more than an hour, then was on his or her back for about the next two hours (“facing up”). This simple plot of position-vs-time may be incorporated into a PSG-style plot by, for example, replacing one of the 17 panes plotted in FIG. 1A or one of the 4 panes plotted in FIG. 1B with the pane plotted in FIG. 2 (and adjusting the time axis as necessary, of course). Such a substitution, however, retains most of the shortcomings present in FIG. 1A and FIG. 1B.
Some reduced sensor devices collect sound as a physiological parameter for use in assessing breathing disorders of sleep, as taught in co-pending U.S. patent application Ser. No. 11/094,911. One factor in the visualization of digitized sound data is the high typical sampling rate, e.g. 2000 samples per second. Thus, in an 8-hour period, over 57 million sound samples may be collected. Although this may be considered a large data set in many visualization applications, there are several examples where signals similar to raw sound are plotted on a common time axis with other physiological signals.
In some cases the envelope of an audio signal may be plotted to give an indication of the loudness of the sound. (Note: we treat sound level, sound intensity, and sound loudness as the same concept herein.) However, the sampling rate of an envelope of an audio signal is often significantly lower than the sampling rate of the signal on which it was based. Thus, the envelope may be plotted similarly to some of the channels in FIG. 1A and FIG. 1B, but with some of the same shortcomings discussed for those figures.
Potsic (Laryngoscope. 1987;97: 1430-1437) (Otolaryngol Head Neck Surg. 1986;94:476-480) teaches a method for representing several minutes of sound data collected by a reduced sensor device. His approach directly represents a quantity related to sound intensity and, indirectly, respiratory regularity. Furthermore, the example plots he provides do not include data from a channel other than sound, which is likely to be a shortcoming of his approach should comparison of sound and other channels be desired.
Other approaches to visualization of sound provide a binary representation of whether sound level (or intensity) have exceeded a certain threshold (Stoohs and Guilleminault. Eur Respir J. 1990;3:823-829) (Penzel et al. Sleep. 1990;13:175-182) (U.S. Pat. Nos. 4,982,738; 5,275,159; and 6,120,441). While potentially compact, this degree of data reduction may be associated with an undesirable loss of information in some applications.
From the above, it is desirable to have improved techniques for characterizing health related disorders.